Deep Convolution Neural Networks (Deep CNNs) are the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve the problem of character recognition, but their use has become as widespread as it is now thanks to recent research. These deep CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolution neural network became a very useful tool in the field of the machine learning.
Meanwhile, image segmentation is a method for receiving an input image such as a training image or a test image and generating a label image as an output image. Recently as the deep learning technology becomes widespread, the deep learning is frequently used for the segmentation.
When the segmentation is performed, if a distribution of the number of pixels is uneven in each cluster on the image, a portion with a small number of pixels is often blurred. A representative example of the uneven distribution of the number of pixels is an image taken by an autonomous vehicle system. In this case, if a specific lane is discontinuous or exists in a remote area in the image, the number of pixels of the specific lane is small, and thus there is a problem that the specific lane where the number of pixels is small is erroneously judged as not a lane at the time of the segmentation.